Private Car Ownership Research Articles

Private Car Ownership in Presence of Shared Autonomous Vehicles, Case of Tehran

Private Car Ownership in Presence of Shared Autonomous Vehicles, Case of Tehran

Shared mobility and lifestyles as mechanisms to reduce environmental impacts from passenger transportation

Despite the deployment of low- or zero-emission technologies, achieving emissions reductions in the passenger transportation sector remains challenging. Demand-side mechanisms can be instrumental in reducing environmental impacts of transportation and reconfiguring transportation systems in a way that shifts users away from private car ownership. In this article we look at the Netherlands, Sweden, and the United States to quantify the environmental benefits from such shifts in passenger transportation, considering socio-technological drivers of transportation including well-being, digitalization, shared mobility, and electrification. We establish pathways for each of these countries considering their context. We frame these pathways using the avoid-shift-improve framework which shapes the scenarios that we quantify in our analysis. We use a travel demand model as an input to calculate carbon, energy, and air pollution footprints. We quantify direct emissions considering the characteristics of the private fleet and indirect using multiregional input-output analysis. The results show that target thresholds can be reached under the proposed supply and demand initiatives. For the United States, these actions are more dramatic than for the Netherlands and Sweden due to that country’s stronger car dependence. A deep social transformation is needed to make these scenarios possible and enable a shift towards public, active and shared transportation in urban areas.

Private car ownership in Indonesia: Affecting factors and policy strategies

Without transportation demand management, urban development and population growth could lead to an increase in the use of private vehicles. Ultimately, increased internal and inter-city traffic causes numerous transportation and social issues. This study sought to develop strategies to limit car ownership in Bandung, Jakarta, and Surabaya, three of the largest cities in Indonesia, by examining the factors that influence car ownership. We conducted external and internal analyses as the basis for a SWOT analysis for developing policy strategies. The external and internal analyses were conducted to understand the external and internal factors that influence transportation conditions in each city. The external analysis was conducted using two models, (1) binomial logistic regression (BLR) and (2) multiple linear regression (MLR) based on user surveys, while the internal analysis was derived from secondary data such as the cities’ development plans. Based on the binomial logistic regression analysis of 585 responses, we found various factors affecting the tendency of owning a car, such as socioeconomic background, garage capacity, as well as perception and attitude toward cars and neighborhood conditions. The multiple linear regression analysis supported the results of the binomial logistic regression analysis by revealing the relationship between variables that influence the number of cars owned. Based on these influencing factors and secondary data, we then conducted a SWOT analysis to develop a number of policy strategies to reduce car ownership. These strategies were specifically designed for the three respective cities. Cities with similar characteristics, such as the major cities of Southeast Asia, could also implement the same strategy.

Carsharing users’ behaviour and attitudes. The role of car availability in households

This paper provides insights into differences in carsharing users' attitudes, motives for joining carsharing, and transport behaviour between users with and without another car at their disposal. It builds on revealed and stated data about members of the oldest carsharing company in the Czech Republic. Carsharing adopters without a car utilise shared cars more intensively than carsharing users with a car available in their household. On the other hand, unlike the second group, they drive fewer kilometres by car in total. The car availability in households also influences the shift in car use after joining carsharing. The sale of a car thanks to adopting carsharing is a factor leading to a decrease in overall car use. Those who have a car at their disposal within their household have a lower probability of decreasing kilometres driven after joining carsharing. Households without an additional car available seem to be less car-dependent on average than those utilising carsharing as a second or third car. They tend to be more environmentally conscious and more inclined towards policies supporting alternative modes and restricting private car use, although both groups share these beliefs. The findings open a debate over whether carsharing increases the legitimacy of restrictive transport measures against private car ownership and use.

Policy dissonance when wearing the “employer hat”: The practice of governmental vehicle reimbursement among public sector employees

Transport-related benefits can have a significant impact on employees’ vehicle ownership and use. Policy implemented by governments, which are often both large-scale employers and models for the private sector, are especially important in determining travel behavior. The research focuses on the Israeli practice of providing substantial subsidies for public sector employees to own cars, through the salary benefit of “vehicle reimbursement”. The benefit, originally created to circumvent salary caps, today provides a substantial income supplement for more than half of the country’s public sector workers, translating to 10% of all Israeli employees (in addition to 10% of employees who receive a company car). The article explores the extent to which vehicle reimbursement encourages car ownership and car usage among public workers. It also seeks to assess the willingness of workers, the labor union and the government to replace the vehicle reimbursement salary component for a substitute that does not require car ownership. Analysis of both a survey of employees (n = 276) and interviews with government and labor union officials, suggests that Israel’s “vehicle reimbursement” constitutes a classic perverse incentive, driving and perpetuating private car ownership and usage, thereby affecting workers’ transport preferences in both the short and long terms. While the majority of public employees are willing to consider accepting salary compensation in lieu of car ownership, institutional and legal obstacles currently stand in the way of policy reform. This case study offers a first exploration of the role of historical employment arrangements in shaping employee and employer transportation decisions.

The Value of Second-hand Basic Passenger Vehicles Based on Factor Analysis

Contemporarily, applications of big data analysis have been implemented to many fields, and the discussion on its theoretical concept and application value is unprecedented. The financial sector is also actively promoting digital construction. To be specific, the construction of financial digitalization is conducive to breaking the drawbacks of traditional accounting, realizing the deep integration of business and finance, and improving the work and efficiency of accounting. Moreover, with China's economic development, the private car ownership will maintain a strong growth momentum. In this paper, we investigate the value of secondhand basic passenger vehicles based on factor analysis. Specifically, data of used cars is used to analyze the influencing factors of second-hand car price. According to the analysis, it is concluded that the used car price factors are Performance factor, time factor and manipulation factor. Moreover, sellers should establish a service system to ensure the quality of second-hand cars and make a reasonable price. These results shed light on guiding further exploration of setting reasonable prices for used cars.

Research on Traffic Congestion Forecast Based on Deep Learning

In recent years, the rapid economic development of China, the increase of the urban population, the continuous growth of private car ownership, the uneven distribution of traffic flow, and the local congestion of the road network have caused traffic congestion. Traffic congestion has become an inevitable problem in the process of urban development, bringing hazards and hidden dangers to citizens’ travel and urban development. The management of traffic congestion first lies in the accurate completion of the identification of road traffic status and the need to predict road congestion in the city, so as to improve the use rate of urban infrastructure road facilities and better alleviate road congestion. In this study, a deep spatial and temporal network model (DSGCN) for predicting traffic congestion status is proposed. First, our study divides the traffic network into grids, where each grid represents a different independent region. In this paper, the centroids of the grid regions are abstracted as nodes, and the dynamic correlations between the nodes are expressed in the form of adjacency matrix. Then, Graph Convolutional Neural Network is used to capture the spatial correlation between regions and a two-layer long and short-term feature model (DSTM) is used to capture the temporal correlation between regions. Finally, the DSGCN outperforms other baseline models and has higher accuracy for traffic congestion prediction as demonstrated by experiments on real PeMS datasets.

Alternative travel mode for commuters with disability in Accra: The lived experiences of Uber and taxi users

This paper explores the alternative travel mode choice among persons with disabilities (PWDs) in the city of Accra. With a limitation in the ownership of private cars among PWDs, public transport plays a critical role in connecting them to various life enhancing opportunities. Despite its importance, the unresponsive nature of Ghana's public transport services and poor attitudes towards the disability community have induced the need for a reliable alternative travel mode hence the need to examine factors that influence this trend in Accra. Using a qualitative research design, this study engaged 28 visually and 22 physically impaired participants through convenience sampling and snowballing. Findings revealed that the lower fees charged for conventional public transport services informed their patronage but the ease, convenience, exclusive services and the friendly reception from Uber drivers remained the key attraction for the increasing trend in their patronage of such services. Unfortunately, higher fees associated with Uber services translated to few trips and fewer social engagements. The study recommends an overhaul of Ghana’s public transport system by ensuring strict compliance to the transport provision of Ghana’s Disability Act given its potential to increase PWDs’ travel options as seen in the enjoyment of lower transport fares.

Drinking water accessibility typologies in low- and middle-income countries

We present a data-driven typology framework for understanding patterns in drinking water accessibility across low- and middle-income countries. Further, we obtain novel typology-specific insights regarding the relationships between possible explanatory variables and typology outcomes. First, we conducted exploratory factor analysis to obtain a smaller set of interpretable factors from the initial set of 17 drinking water accessibility indicators from 73 countries. The resulting seven factors summarize the key drivers for water accessibility, and also serve as a vehicle for framing discussions on country outcomes. We clustered the countries based on their seven-dimensional water accessibility factor scores, referring to the resulting three clusters as ‘typologies,’ namely, Decentralized, Centralized and Hybrid. The typologies serve as a vehicle for analyzing water accessibility among countries with similar patterns, in contrast with geographically-based approaches. Finally, we fitted a decision tree classifier to analyze relationships between a country’s typology membership and socioeconomic, geographic and transportation explanatory variables. We found that private car ownership, population density and per-capita gross domestic product are most relevant in predicting a country’s drinking water accessibility typology.

An interval feature discrete grey-Markov model based on data distributions and applications

Data sequences represent complex changes in external environments or internal systems, and patterns of some index sequences have become more difficult to characterize using real number sequences. Considering the imbalances between regional development levels in terms of socioeconomic indicators, a discrete grey-Markov model based on interval distribution characteristics is proposed in this paper. Based on the classic discrete grey model, a difference coefficient sequence and median sequences of the upper and lower information domains, respectively, are introduced to establish differential equations for the representation and prediction of different development level trends. Furthermore, a combination of discrete grey models with the Markov model is used to modify the nonstationary data sequences and further improve the new model prediction accuracy. The parameter estimation and solution of the proposed model are organized using analytical methods, and the accuracy of the model is investigated. By fitting and predicting examples of private car ownership and milk production in representative unbalanced development regions, the results of the proposed model are compared with those of four other prediction models to verify its effectiveness. The results demonstrate that the prediction accuracy of the proposed model is better than those of other grey models in nonstationary data sequences. By capturing the interval features, this model achieves the goal of characterization of interval distribution information and further modification of the nonstationary series.

Effect and spatial differentiation of vehicle restriction policy on urban private car ownership: Empirical evidence based on the multiphase difference-in-differences model

Effect and spatial differentiation of vehicle restriction policy on urban private car ownership: Empirical evidence based on the multiphase difference-in-differences model

Economic impacts, carbon footprint and rebound effects of car sharing: Scenario analysis assessing business-to-consumer and peer-to-peer car sharing

Car sharing is promoted as one alternative to decrease emissions from personal transport in cities. Most emission assessments have focused on the production and use phase of car sharing, but few have focused on the impacts of changes in consumption. In this article I assess changes in the total output from various sectors and in the carbon footprint of car sharing users from a consumption perspective. This study applied a mixed top-down and bottom-up approach using scenario analysis. It is based on a detailed expenditure analysis that links changes in expenditure to variations in the total output of sectors and the carbon footprint of personal mobility using environmentally extended multi regional input and output analysis. The results show that car sharing can change the total output of several sectors, with traceable climate consequences. Variations in total outputs from sectors depend on existing national economic structures. Car sharing users who abandoned private car ownership can decrease their carbon footprint of transport by approximately 40 %. Car sharing users with a car-free lifestyle who started using car sharing for 3 % of their vehicle kilometres travelled increased their transport carbon footprint by 0.42 % to 0.70 %. Meanwhile, the rebound effects of re-spending can be as high as 70 to 85 %. This indicates that the potential carbon savings from car sharing may be drastically reduced by non-transport related consumption. Differences between the carbon footprints of business-to-consumer and peer-to-peer car sharing users were marginal.

GPS-based analysis of shared e-mobility services

Car sharing aims to reduce private car ownership and to promote multimodal transport, so that active and public transport gain importance as part of the transport system and car journeys are only made when there is a specific need for this mode of transport. This study analyzes GPS tracks of car sharing trips in a high-density urban setting to gain insights into the sustainability and efficiency of e-car sharing services. The analyses show that sporadic users in particular fulfill the sustainability purpose of the car-sharing service, while the behavior of frequent users seems to include additional trips induced by the service or trips that could easily be covered by more environmentally friendly transport modes. Applying the method to a variety of differently designed car-sharing services promises to enable further insights.

A novel simultaneous grey model parameter optimization method and its application to predicting private car ownership and transportation economy

<p style='text-indent:20px;'>Common models used for grey system predictions include the GM (1, 1), GM (1, N), and GM (N, 1). Their whitening equations are ordinary differential equations. However, the objects and factors are generally characterized by mutual restrictions and variable interactions. Thus, the relationship cannot be adequately described using a single differential equation. Therefore, this paper proposes a novel simultaneous grey model. A parameter optimization method is developed, and the time response equation of a simultaneous grey model with 2 interactive variables is derived. The model has high simulation and prediction precision because the parameter optimization's objective function minimizes the average relative errors of the simulation and prediction. The proposed model with two mutually influencing variables is used to predict the private car ownership and the added value of the transportation industry in China. The results indicate that the simultaneous grey model has significantly higher precision than the conventional single grey model. The novel model and method improve the grey modeling system and are significant for in-depth studies, popularization, and application of grey models.</p>

Do Household Characteristics Influence Private Car and Motorcycle Ownership? Evidence from a Case Study in Yogyakarta Urban Area, Indonesia

Yogyakarta is listed among the cities with the worst congestion in Indonesia. Besides, the development of Yogyakarta has now expanded to the surrounding area and agglomerated into the Yogyakarta Urban Area (KPY). Private vehicle ownership is closely correlated to the characteristics of households. This study aims to analyze the correlation of the level of private vehicle ownership with the characteristics of households and the correlation of private vehicle ownership towards the responses of households with transportation policies that will be implemented in the future. This study fully used secondary data. The total respondents were 8,810 households, and the research location was the Yogyakarta Urban Area (KPY). The path analysis was employed to find out the causal relation between variables and the contribution between variables in the analysis. The variables used were gender, age, education level of the heads of households, total household income, the number of adult family members ( 17 years old), the number of children (17 years old), the number of working family members, total motorcycle licenses in a household, total driver's licenses in a household, total motorcycle ownership in a household, total car ownership in a household, and the responses of households to the transportation policies that will be applied. The results of the study show that motorcycle ownership in a household is influenced by gender, age, the level of education of the heads of households, total household income, the number of adult family members, the number of children, the number of working family members, and total motorcycle licenses in a household. Meanwhile, car ownership in a household is influenced by age, total household income, the number of working family members, and total driver's licenses in a household. There is also a correlation between motorcycle and car ownership. Increasing car ownership in a household has a positive correlation with motorcycle ownership. Furthermore, motorcycle ownership has a good significance value and a positive correlation with pedestrianization policies prioritizing public transportation in the Mangkubumi-Malioboro-Kraton area.

Human Centred Design Considerations for the Development of Sustainable Public Transportation in Malaysia

The growing trend of private car ownership in Malaysia is increasing traffic congestion. As part of a solution, the Malaysian Automotive Association (MAA) chose to develop technologies for an electric bus system in Kuala Lumpur and initiated a project to design and engineer an electric bus system with a focus on Human Centred Design (HCD) principles. In order to fulfil this a total of 28-hours of user observation was conducted in Kuala Lumpur (KL), Malaysia. Alongside this field research an online questionnaire study of future users (n = 102, Age 21–35) was conducted to further substantiate findings. As a result, we address the need for HCD considerations to be included in sustainable public transport systems in Malaysia. User observation revealed six key issues associated with the daily routine of a commuter which were then used to inform the questionnaire survey. Alarmingly, the survey revealed that over 60 per cent of respondents had never used the bus system in Kuala Lumpur stating problems such as frequency, comfort, driver attitude and bus shelter access. This provided a necessary platform for improvement resulting in eight key recommendations ranked by importance with the aim to increase bus usage in Malaysia.

Is there a stronger willingness to pay for air quality improvement with high education: new evidence from a survey in China.

As a developing country with the largest population and serious environmental pollution in the world, China has made great efforts in air pollution. Air quality improvement depends not only on government administrative regulations but also on public support, especially how much the public is willing to pay for air quality improvement. Higher education will encourage the public to take action to improve air quality. However, the confirmation of the causality relationship between WTP and education has been missing. This study uses the Chinese General Social Survey (CGSS) to find the relationship between the two, and the conclusions are drawn: OLS regression model and instrumental variable both determine the positive influence of education level on air quality improvement WTP, and Heckman model further verifies the robustness of the conclusion. The positive influence of education level is greater in the groups of men, higher income, higher awareness of acid rain, and more air purifiers, and it has a greater impact on married people in rural areas than in urban areas. The function mechanism of education can improve residents' WTP by increasing regional GDP, promoting urbanization level, expanding afforestation areas, decreasing private car ownership and the number of newly registered civil cars, and reducing sulfur dioxide emissions, nitrogen oxides, and smoke (powder) dust. The total social and economic value of air quality improvement in China is 34.572 billion CNY to 672.42 trillion CNY.

Urban Customized Bus Design for Private Car Commuters

With the deepening of the urbanization process, the ownership of urban private cars continues to increase, resulting in severe urban traffic congestion and environmental problems. The customized bus, as an emerging public transportation mode, is considered an effective means to alleviate the above problems. This article employs electronic registration identification (ERI) data of vehicles to design customized buses for private cars, consisting of two components: 1) discovering private car commuters and 2) designing customized bus schemes. First, based on the spatial–temporal similarity and high-frequency characteristics of commuting trips, we mined the urban private car commuters and their corresponding commuting trips as the demand for customized buses. Then, we constructed the customized bus model, which targets the number of served passengers with the constraints, such as the trip time window, bus capacity, passenger load rate, etc. In the model, intermediate stops are not set to ensure bus punctuality and passenger experience, and buses of various capacities are employed to ensure effectiveness and efficiency. The differential evolution algorithm was utilized to find the optimal solution for the model. In the experiments, we carried out relevant verification based on Chongqing’s one-week ERI data. The experimental results showed customized bus schemes for various cases and verified the superior performance of our algorithm by comparing it with general optimization algorithms. Besides, through numerical calculation and traffic simulation, the excellent potential for customized buses in reducing urban transportation energy consumption and urban road congestion is illustrated.

The varying roles of the dimensions of affluence in air pollution: a regional STIRPAT analysis for Germany

STIRPAT models investigate the impacts of population, affluence, and technology on the environment, with most STIRPAT studies revealing positive impacts of both population and affluence. Affluence is commonly defined as GDP per capita, but investigations of its impact largely neglect the possibility that increasing prosperity affects the environment in varying—even opposing—ways. This study addresses this gap by decomposing affluence into three dimensions—income per taxpayer, private car ownership, and the share of single-family houses—and analyzing their roles in the production of local NOx emissions. Results for 367 German districts and autonomous cities between 1990 and 2020 indicate that, while private car ownership and single-family houses per capita can be considered drivers of local pollutants, such is not the case for income per taxpayer, which we find has a negative impact on NOx emissions. The empirical findings suggest that policies should strengthen integrated mobility concepts and establish incentives that favor investment in modern heating or self-sufficiency systems.

Exploring Human Mobility Patterns and Travel Behavior: A Focus on Private Cars

The ever-increasing number of automobiles, especially private cars, has irrevocably altered social life, revolutionized travel experience, and reshaped the face of urban transportation systems. Existing research mainly focuses on trajectory data from floating cars, while human mobility and travel behavior by private cars are still little understood. To bridge this gap, we employ a private car trajectory data set collected from real-world urban environments. This article provides a new perspective on human mobility, with a focus on private cars, by investigating mobility patterns and capturing the spatiotemporal evolution of urban hot zones from people’s arrive–stay–leave (ASL) behavior. Understanding these hot zones and travel behavior patterns is necessary when considering changes or improvements in urban networks. Decisions impacting the safety and emissions impacts of urban travel, including, for example, design improvements at curves or to reduce left-turn conflicts, improvements to pedestrian facilities, and the placement of alternative energy refueling stations, will rely on similar analyses across many different urban areas. Our article provides a proof of concept, and we then outline new research opportunities and challenges involving private car trajectory data.Along with the increased process of urbanization and industrialization, recent years have witnessed an ever-increasing number of automobiles, especially private cars <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , also known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passenger cars</i> , those with less than seven seats and usually registered to individuals for personal use. Compared to floating cars <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , private cars constitute the vast majority of automobiles. For instance, the nationwide ownership of automobiles in China exceeded 263 million by the end of 2019 <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , and more than 88% of these vehicles (225 million) were registered to individuals. As shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1(a)</xref> , ownership of private cars exhibited a growing trend from 2013 to 2019. As seen in Sivak’s technical report <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> , per capita car ownership in the United States was 0.756 in 2015 and increased to 0.766 by 2016. According to <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> , in the last two decades, private cars accounted for more than 75% of passenger transport activity in the European Union. As shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1(b)</xref> , even though a slightly decreasing trend is expected by 2030, it is still expected to be larger than 72%.